Land-use planning following resource extraction – lessons from grizzly bears at reclaimed and active open pit mines
Bibliographic record
Abstract
Gauging the success of industrial reclamation requires targets to be set for restoring ecosystem structure and function. An indication of reclamation success is if wildlife recolonise, forage, rest, reproduce and survive on reclaimed areas. The grizzly bear is a threatened species that exists at low densities in Alberta, Canada and facilitates a variety of ecosystem processes. To make suggestions for mitigating the effects of open pit mining on this species, we collected and analysed biological data for grizzly bears on and around Cheviot, Luscar and Gregg River coal mines in west-central Alberta. During 2008–2010, we captured and attached GPS radio collars on 12 adult bears on and around mines which allowed us to intensively track their movements. We visited bear-used GPS locations in the field to assess bear activity and microhabitat characteristics. Bears selected reclaimed mines and areas near mines extensively in late spring and early summer to forage on forbs sown as part of mine reclamation and to depredate ungulate calves and lambs. In the fall, bears moved primarily in areas outside mines to forage on berries in preparation for winter denning. Bears often bedded in dense tree cover which underlines the importance of maintaining original vegetation patches in planning mine operations. The animals sometimes crossed the major active mine haul road and moved on and near trails designated for human access on mine leases. High mortality risk associated with expansion of human access into previously remote areas is a major threat to long-term persistence of the grizzly bear population. Defensive driving and potentially enforcement of speed restrictions on mine haul roads in areas with high frequency of bear crossings, provisioning for ecological movement corridors and proper waste management practices will help prevent human-bear conflict during the active mining phase. Following closure of mines, access management along designated trails will reduce the risk of conflicts. Imposing access restrictions, along with preserving undisturbed habitat patches and restoring the original vegetation cover will enable coexistence of people and bears on a shared landscape.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".